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Semantic similarity between tow sentences in arabic

ايجاد نسبة التشابه الدلالي بين جملتين باللغة العربية

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 Publication date 2018
and research's language is العربية
 Created by Khadija Mohammad




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Text Similarity is an important task in several application fields, such as information retrieval, plagiarism detection, machine translation, topic detection, text classification, text summarization and others. Finding similarity between two texts, paragraphs or sentences, is based on measuring, directly or indirectly, the similarity between words. There are two known types of words similarity: lexical and semantic. The first one handles the words as a stream of characters: words are similar lexically if they share the same characters in the same order. The second type aims to quantify the degree to which two words are semantically related. As an example they can be, synonyms, represent the same thing or they are used in the same context. In this article we focus our investigation on measuring the semantic similarity between Arabic sentences using several representations


Artificial intelligence review:
Research summary
تتناول هذه الورقة البحثية موضوع إيجاد نسبة التشابه الدلالي بين جملتين باللغة العربية، وهو موضوع ذو أهمية كبيرة في مجالات متعددة مثل استرجاع المعلومات، الكشف عن الانتحال، الترجمة الآلية، واستخراج المعلومات. تقدم الورقة عدة تقنيات لحساب هذا التشابه، مع التركيز على استخدام قاعدة بيانات معجمية تحتوي على جميع كلمات اللغة العربية وعلاقاتها. تتناول الورقة ثلاث طرق رئيسية لقياس التشابه: استخدام WordToVector، استخدام LMF Dictionaries، واستخدام خوارزمية Wu & Palmer. تتضمن كل طريقة مجموعة من الخطوات والتقنيات الفرعية مثل استخدام IDF وPOS_tagging لتحسين دقة النتائج. كما تستعرض الورقة كيفية تمثيل الكلمات كأشعة في فضاء متعدد الأبعاد واستخدام تقنيات مثل Word2vec وCBOW لتدريب النماذج على نصوص كبيرة. تقدم الورقة أيضًا مقارنة بين النتائج التي تم الحصول عليها باستخدام الطرق المختلفة وتوضح كيفية تحسين النتائج باستخدام تقنيات مثل IDF وPOS_tagging.
Critical review
تعتبر هذه الورقة خطوة مهمة نحو تحسين تقنيات معالجة اللغة الطبيعية باللغة العربية، وهي تقدم حلولًا مبتكرة ومفصلة لمشكلة حساب التشابه الدلالي بين الجمل. ومع ذلك، يمكن تحسين الورقة من خلال تقديم مزيد من التفاصيل حول كيفية اختيار المعايير المختلفة لتدريب النماذج، وكذلك تقديم أمثلة عملية توضح كيفية تطبيق هذه التقنيات في سياقات حقيقية. كما يمكن تحسين الورقة من خلال تقديم تحليل نقدي للقيود والتحديات التي تواجه هذه التقنيات، مثل التعامل مع اللهجات المختلفة للغة العربية والتحديات المرتبطة بمعالجة النصوص الكبيرة.
Questions related to the research
  1. ما هي الأهمية الرئيسية لحساب التشابه الدلالي بين الجمل باللغة العربية؟

    الأهمية الرئيسية لحساب التشابه الدلالي تكمن في تطبيقات متعددة مثل استرجاع المعلومات، الكشف عن الانتحال، الترجمة الآلية، واستخراج المعلومات.

  2. ما هي الطرق الثلاث الرئيسية التي تم استخدامها في الورقة لقياس التشابه الدلالي؟

    الطرق الثلاث الرئيسية هي: استخدام WordToVector، استخدام LMF Dictionaries، واستخدام خوارزمية Wu & Palmer.

  3. ما هي التقنيات المستخدمة لتحسين دقة النتائج في حساب التشابه الدلالي؟

    التقنيات المستخدمة تشمل IDF وPOS_tagging لتحسين دقة تحديد الكلمات التي تكون وصفية للغاية في كل جملة.

  4. ما هي التحديات التي يمكن أن تواجه تقنيات حساب التشابه الدلالي بين الجمل باللغة العربية؟

    التحديات تشمل التعامل مع اللهجات المختلفة للغة العربية والتحديات المرتبطة بمعالجة النصوص الكبيرة.


References used
http://aclweb.org/anthology/W17-1303
https://en.wikipedia.org/wiki/Word2vec
https://github.com/bakrianoo/aravec
https://rd.springer.com/article/10.1007/s40595-016-0080-2
https://trac.research.cc.gatech.edu/ccl/export/158/SecondMindProject/SM/SM.WordNet/Paper/WordNetDotNet_Semantic_Similarity.pdf
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